Scientists uncover brain mechanisms underlying schizophrenia and bipolar disorder

Both schizophrenia and bipolar disorder disrupt thought and emotion, but their underlying biology has remained elusive because no single brain region or enzyme can pinpoint the problem. To bridge this gap, lead researcher Dr. Annie Kathuria, a biomedical engineer at Johns Hopkins, and her team turned to brain organoids—pea-sized clusters of neurons grown from a patient’s blood or skin cells that mimic the structure and function of the developing human brain.

Using induced pluripotent stem cells (iPSCs) from twelve participants—some with schizophrenia, some with bipolar disorder, and healthy controls—the researchers cultured organoids representing the prefrontal cortex, the brain’s decision-making hub. They placed these organoids on microchips fitted with multi-electrode arrays, enabling them to record tiny bursts of electrical activity, much like a miniature EEG. Then, they applied machine learning algorithms to decode patterns that distinguished diseased organoids from healthy ones.

The models identified unique neural “signatures” for each disorder, including differences in firing rates, rhythmic timing, and spike distribution. Remarkably, the system was able to correctly identify whether an organoid came from a healthy or diseased individual with 83 percent accuracy—a figure that rose to 92 percent after mild electrical stimulation revealed hidden neural dynamics.

These findings mark a potential turning point in psychiatry. By expanding this work, clinicians might one day test new medications directly on patient derived organoids, optimizing drug doses without trial and error.

As the team collaborates with neurosurgeons and psychiatrists to analyze more samples, their next goal is equally ambitious: using these lab-grown “mini brains” to predict which drugs—and at what concentrations—will restore healthy neural activity, moving psychiatry closer to a future of personalized, biology based mental healthcare.

REFERENCE: “Machine learning-enabled detection of electrophysiological signatures in iPSC-derived models of schizophrenia and bipolar disorder” by Kai Cheng, Autumn Williams, Anannya Kshirsagar, Sai Kulkarni, Rakesh Karmacharya, Deok-Ho Kim, Sridevi V. Sarma and Annie Kathuria, 22 September 2025, APL Bioengineering; DOI: 10.1063/5.0250559